Predictive value of early magnetic resonance imaging measures is differentially affected by the dose of interferon beta-1a given subcutaneously three times a week: an exploratory analysis of the PRISMS study
Bibliographic record
Abstract
BACKGROUND: On-treatment magnetic resonance imaging lesions may predict long-term clinical outcomes in patients receiving interferon β-1a. This study aimed to assess the effect of active T2 and T1 gadolinium-enhancing (Gd+) lesions on relapses and 3-month confirmed Expanded Disability Status Scale (EDSS) progression in the PRISMS clinical trial. METHODS: Exploratory analyses assessed whether active T2 and T1 Gd + lesions at Month 6, or active T2 lesions at Month 12, predicted clinical outcomes over 4 years in PRISMS. RESULTS: Mean active T2 lesion number at Month 6 was significantly lower with interferon beta-1a given subcutaneously (IFN β-1a SC) 44 μg and 22 μg 3×/week (tiw) than with placebo (p < 0.0001). The presence of ≥4 versus 0 active T2 lesions predicted disability progression at Years 3-4 in the IFN β-1a SC 22 μg group only (p < 0.05), whereas the presence of ≥2 versus 0-1 active T2 lesions predicted disability progression in the placebo/delayed treatment (DTx) (Years 2-4; p < 0.05) and IFN β-1a SC 22 μg groups (Years 3-4; p < 0.05). Greater active T2 lesion number at 6 months predicted relapses in the placebo/DTx group only (≥4 vs. 0, Years 1-4; ≥2 vs. 0-1, Years 2-4; p < 0.05), and the presence of T1 Gd + lesions at 6 months predicted disability progression in the IFN β-1a SC 44 μg group only (Year 1; p < 0.05). The presence of ≥2 versus 0-1 active T2 lesions at 12 months predicted disability progression over 3 and 4 years in the IFN β-1a SC 44 μg group. CONCLUSION: Active T2 lesions at 6 months predicted clinical outcomes in patients receiving placebo or IFN β-1a SC 22 μg, but not in those receiving IFN β-1a SC 44 μg. Active T2 lesions at 12 months may predict outcomes in those receiving IFN β-1a SC 44 μg and are possibly more suggestive of poor response to therapy than T2 results at 6 months.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".